#INSTALAMOS LAS LIBRERIAS A UTILIZAR

install.packages("hexbin")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("readr")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("ggplot2")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("dplyr")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("scatterplot3d",dependencies=T)
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("tidyverse")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)

#CARGAMOS LAS LIBRERIAS QUE VAMOS A UTILIZAR

library(readr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(hexbin)
library(scatterplot3d)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble  3.0.6     ✓ stringr 1.4.0
## ✓ tidyr   1.1.2     ✓ forcats 0.5.1
## ✓ purrr   0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

#OBTENEMOS EL ENLACE DE LA CARPETA EN LA QUE ESTAMOS TRABAJANDO

getwd()
## [1] "/cloud/project/R-MARKDOWN"

#BUSCAMOS LA RUTA DEL DATAFRAME

cafe_df<-read.csv("/cloud/project/R-MARKDOWN/cafesito/produccion.csv", header = TRUE, sep=",", dec=".")

#NOS INDICA EL TIPO O CLASE DE LA VARIABLE RENDIMIENTO

class(cafe_df$Rendimiento..ha.ton.)
## [1] "numeric"

#A CONTINUACION PODEMOS IDENTIFICAR LA ESTRUCTURA DEL DATAFRAME

str(cafe_df)
## 'data.frame':    266 obs. of  8 variables:
##  $ Anio                     : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
##  $ Departamento             : chr  "ANTIOQUIA" "BOLIVAR" "BOYACA" "CALDAS" ...
##  $ Producto                 : chr  "CAFE" "CAFE" "CAFE" "CAFE" ...
##  $ Area..ha.                : chr  "112,343.60" "502" "11,374.50" "78,393.65" ...
##  $ Produccion..ton.         : chr  "120,500.80" "446" "9,683.10" "92,815.00" ...
##  $ Rendimiento..ha.ton.     : num  1.07 0.89 0.85 1.18 0.93 0.79 0.96 0.57 0.71 0.78 ...
##  $ Produccion.Nacional..ton.: num  14.54 0.05 1.17 11.2 0.26 ...
##  $ Area.Nacional..ha.       : num  14.66 0.07 1.48 10.23 0.3 ...

#CONOCER LA DIMENSION DEL DATAFRAME

dim(cafe_df)
## [1] 266   8

#PODEMOS VER LA INFORMACION RESUMIDA DEL DATAFRAME DE LAS VARIABLES CUANTITATIVAS Y VALORES ESTADISTICOS

summary(cafe_df)
##       Anio      Departamento         Producto          Area..ha.        
##  Min.   :2007   Length:266         Length:266         Length:266        
##  1st Qu.:2010   Class :character   Class :character   Class :character  
##  Median :2012   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :2012                                                           
##  3rd Qu.:2015                                                           
##  Max.   :2018                                                           
##  Produccion..ton.   Rendimiento..ha.ton. Produccion.Nacional..ton.
##  Length:266         Min.   :0.0000       Min.   : 0.0000          
##  Class :character   1st Qu.:0.7500       1st Qu.: 0.3525          
##  Mode  :character   Median :0.9400       Median : 2.7200          
##                     Mean   :0.9364       Mean   : 4.5113          
##                     3rd Qu.:1.1200       3rd Qu.: 7.1475          
##                     Max.   :2.0000       Max.   :18.6700          
##  Area.Nacional..ha.
##  Min.   : 0.000    
##  1st Qu.: 0.390    
##  Median : 3.120    
##  Mean   : 4.511    
##  3rd Qu.: 6.875    
##  Max.   :16.430

#INFORMACION CUANTITATIVA DEL DATAFRAME USANDO VARIAS VARIABLES

tapply(cafe_df$Rendimiento..ha.ton., cafe_df$Anio, summary)
## $`2007`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4500  0.7900  0.9000  0.9505  1.1525  1.5300 
## 
## $`2008`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4500  0.7775  0.9050  0.9827  1.2000  1.7900 
## 
## $`2009`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3000  0.7600  0.9300  0.8814  1.1125  1.2100 
## 
## $`2010`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.7050  0.9600  0.9061  1.1250  1.5200 
## 
## $`2011`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4700  0.6100  0.9000  0.8543  1.0550  1.2000 
## 
## $`2012`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.7450  0.8300  0.8587  0.9150  2.0000 
## 
## $`2013`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6000  0.6000  0.7550  0.7595  0.8800  0.9900 
## 
## $`2014`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6400  0.6500  0.8150  0.8223  0.9500  1.0600 
## 
## $`2015`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.770   0.935   1.065   1.025   1.107   1.150 
## 
## $`2016`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.790   0.960   1.120   1.064   1.150   1.190 
## 
## $`2017`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.660   0.845   1.090   1.068   1.290   1.500 
## 
## $`2018`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6200  0.8575  1.1200  1.0795  1.3125  1.5200

#ALGUNOS VALORES ESTADISTICOS

sum(cafe_df$Rendimiento..ha.ton.)
## [1] 249.09
prod(cafe_df$Area.Nacional..ha.)
## [1] 0
var(cafe_df$Rendimiento..ha.ton.)
## [1] 0.07135814
length(cafe_df$Area.Nacional..ha.)
## [1] 266
mean(cafe_df$Rendimiento..ha.ton.)
## [1] 0.9364286
max(cafe_df$Rendimiento..ha.ton.)
## [1] 2
min(cafe_df$Rendimiento..ha.ton.)
## [1] 0
sd(cafe_df$Rendimiento..ha.ton.)
## [1] 0.2671294

#RESTAR EL VALOR MAXIMO Y EL VALOR MINIMO DE LA VARIABLE RENDIMIENTO

max(cafe_df$Rendimiento..ha.ton.)-min(cafe_df$Rendimiento..ha.ton.)
## [1] 2

#NOMBRE DE LAS VARIABLES DEL DATAFRAME

names(cafe_df)
## [1] "Anio"                      "Departamento"             
## [3] "Producto"                  "Area..ha."                
## [5] "Produccion..ton."          "Rendimiento..ha.ton."     
## [7] "Produccion.Nacional..ton." "Area.Nacional..ha."
colnames(cafe_df)
## [1] "Anio"                      "Departamento"             
## [3] "Producto"                  "Area..ha."                
## [5] "Produccion..ton."          "Rendimiento..ha.ton."     
## [7] "Produccion.Nacional..ton." "Area.Nacional..ha."

#VER LAS PRIMERAS Y ULTIMAS 6 FILAS DEL DATAFRAME

head(cafe_df)
##   Anio Departamento Producto  Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 1 2007    ANTIOQUIA     CAFE 112,343.60       120,500.80                 1.07
## 2 2007      BOLIVAR     CAFE        502              446                 0.89
## 3 2007       BOYACA     CAFE  11,374.50         9,683.10                 0.85
## 4 2007       CALDAS     CAFE  78,393.65        92,815.00                 1.18
## 5 2007      CAQUETA     CAFE   2,295.00         2,134.00                 0.93
## 6 2007     CASANARE     CAFE   2,605.00         2,048.40                 0.79
##   Produccion.Nacional..ton. Area.Nacional..ha.
## 1                     14.54              14.66
## 2                      0.05               0.07
## 3                      1.17               1.48
## 4                     11.20              10.23
## 5                      0.26               0.30
## 6                      0.25               0.34
tail(cafe_df)
##     Anio    Departamento Producto Area..ha. Produccion..ton.
## 261 2018        PUTUMAYO     CAFE    209.93            289.5
## 262 2018         QUINDIO     CAFE 16,374.73        17,739.03
## 263 2018       RISARALDA     CAFE 35,874.73        45,918.75
## 264 2018       SANTANDER     CAFE 42,269.07        55,918.71
## 265 2018          TOLIMA     CAFE 97,304.04        97,451.31
## 266 2018 VALLE DEL CAUCA     CAFE 48,305.31        49,667.88
##     Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 261                 1.38                      0.03               0.03
## 262                 1.08                      2.07               2.21
## 263                 1.28                      5.37               4.83
## 264                 1.32                      6.53               5.69
## 265                 1.00                     11.39              13.11
## 266                 1.03                      5.80               6.51

#INSPECCIONAR DE FORMA DETALLADA EN EL DATAFRAME SELECCIONANDO SUS COLUMNAS Y FILAS

cafe_df[3]
##     Producto
## 1       CAFE
## 2       CAFE
## 3       CAFE
## 4       CAFE
## 5       CAFE
## 6       CAFE
## 7       CAFE
## 8       CAFE
## 9       CAFE
## 10      CAFE
## 11      CAFE
## 12      CAFE
## 13      CAFE
## 14      CAFE
## 15      CAFE
## 16      CAFE
## 17      CAFE
## 18      CAFE
## 19      CAFE
## 20      CAFE
## 21      CAFE
## 22      CAFE
## 23      CAFE
## 24      CAFE
## 25      CAFE
## 26      CAFE
## 27      CAFE
## 28      CAFE
## 29      CAFE
## 30      CAFE
## 31      CAFE
## 32      CAFE
## 33      CAFE
## 34      CAFE
## 35      CAFE
## 36      CAFE
## 37      CAFE
## 38      CAFE
## 39      CAFE
## 40      CAFE
## 41      CAFE
## 42      CAFE
## 43      CAFE
## 44      CAFE
## 45      CAFE
## 46      CAFE
## 47      CAFE
## 48      CAFE
## 49      CAFE
## 50      CAFE
## 51      CAFE
## 52      CAFE
## 53      CAFE
## 54      CAFE
## 55      CAFE
## 56      CAFE
## 57      CAFE
## 58      CAFE
## 59      CAFE
## 60      CAFE
## 61      CAFE
## 62      CAFE
## 63      CAFE
## 64      CAFE
## 65      CAFE
## 66      CAFE
## 67      CAFE
## 68      CAFE
## 69      CAFE
## 70      CAFE
## 71      CAFE
## 72      CAFE
## 73      CAFE
## 74      CAFE
## 75      CAFE
## 76      CAFE
## 77      CAFE
## 78      CAFE
## 79      CAFE
## 80      CAFE
## 81      CAFE
## 82      CAFE
## 83      CAFE
## 84      CAFE
## 85      CAFE
## 86      CAFE
## 87      CAFE
## 88      CAFE
## 89      CAFE
## 90      CAFE
## 91      CAFE
## 92      CAFE
## 93      CAFE
## 94      CAFE
## 95      CAFE
## 96      CAFE
## 97      CAFE
## 98      CAFE
## 99      CAFE
## 100     CAFE
## 101     CAFE
## 102     CAFE
## 103     CAFE
## 104     CAFE
## 105     CAFE
## 106     CAFE
## 107     CAFE
## 108     CAFE
## 109     CAFE
## 110     CAFE
## 111     CAFE
## 112     CAFE
## 113     CAFE
## 114     CAFE
## 115     CAFE
## 116     CAFE
## 117     CAFE
## 118     CAFE
## 119     CAFE
## 120     CAFE
## 121     CAFE
## 122     CAFE
## 123     CAFE
## 124     CAFE
## 125     CAFE
## 126     CAFE
## 127     CAFE
## 128     CAFE
## 129     CAFE
## 130     CAFE
## 131     CAFE
## 132     CAFE
## 133     CAFE
## 134     CAFE
## 135     CAFE
## 136     CAFE
## 137     CAFE
## 138     CAFE
## 139     CAFE
## 140     CAFE
## 141     CAFE
## 142     CAFE
## 143     CAFE
## 144     CAFE
## 145     CAFE
## 146     CAFE
## 147     CAFE
## 148     CAFE
## 149     CAFE
## 150     CAFE
## 151     CAFE
## 152     CAFE
## 153     CAFE
## 154     CAFE
## 155     CAFE
## 156     CAFE
## 157     CAFE
## 158     CAFE
## 159     CAFE
## 160     CAFE
## 161     CAFE
## 162     CAFE
## 163     CAFE
## 164     CAFE
## 165     CAFE
## 166     CAFE
## 167     CAFE
## 168     CAFE
## 169     CAFE
## 170     CAFE
## 171     CAFE
## 172     CAFE
## 173     CAFE
## 174     CAFE
## 175     CAFE
## 176     CAFE
## 177     CAFE
## 178     CAFE
## 179     CAFE
## 180     CAFE
## 181     CAFE
## 182     CAFE
## 183     CAFE
## 184     CAFE
## 185     CAFE
## 186     CAFE
## 187     CAFE
## 188     CAFE
## 189     CAFE
## 190     CAFE
## 191     CAFE
## 192     CAFE
## 193     CAFE
## 194     CAFE
## 195     CAFE
## 196     CAFE
## 197     CAFE
## 198     CAFE
## 199     CAFE
## 200     CAFE
## 201     CAFE
## 202     CAFE
## 203     CAFE
## 204     CAFE
## 205     CAFE
## 206     CAFE
## 207     CAFE
## 208     CAFE
## 209     CAFE
## 210     CAFE
## 211     CAFE
## 212     CAFE
## 213     CAFE
## 214     CAFE
## 215     CAFE
## 216     CAFE
## 217     CAFE
## 218     CAFE
## 219     CAFE
## 220     CAFE
## 221     CAFE
## 222     CAFE
## 223     CAFE
## 224     CAFE
## 225     CAFE
## 226     CAFE
## 227     CAFE
## 228     CAFE
## 229     CAFE
## 230     CAFE
## 231     CAFE
## 232     CAFE
## 233     CAFE
## 234     CAFE
## 235     CAFE
## 236     CAFE
## 237     CAFE
## 238     CAFE
## 239     CAFE
## 240     CAFE
## 241     CAFE
## 242     CAFE
## 243     CAFE
## 244     CAFE
## 245     CAFE
## 246     CAFE
## 247     CAFE
## 248     CAFE
## 249     CAFE
## 250     CAFE
## 251     CAFE
## 252     CAFE
## 253     CAFE
## 254     CAFE
## 255     CAFE
## 256     CAFE
## 257     CAFE
## 258     CAFE
## 259     CAFE
## 260     CAFE
## 261     CAFE
## 262     CAFE
## 263     CAFE
## 264     CAFE
## 265     CAFE
## 266     CAFE
cafe_df[,4]
##   [1] "112,343.60" "502"        "11,374.50"  "78,393.65"  "2,295.00"  
##   [6] "2,605.00"   "53,471.00"  "23,172.00"  "290"        "43,017.30" 
##  [11] "89,661.56"  "4,785.00"   "17,506.00"  "2,048.00"   "24,458.50" 
##  [16] "30,171.84"  "35"         "19,904.00"  "47,689.25"  "34,406.67" 
##  [21] "91,679.10"  "76,667.80"  "114,694.00" "572"        "10,778.50" 
##  [26] "74,897.00"  "2,735.00"   "2,149.00"   "56,208.00"  "23,198.00" 
##  [31] "90"         "43,633.35"  "89,131.20"  "4,553.00"   "17,521.00" 
##  [36] "2,146.00"   "25,582.00"  "30,171.84"  "31"         "19,571.00" 
##  [41] "47,227.00"  "34,169.37"  "86,829.20"  "72,419.00"  "112,420.20"
##  [46] "770"        "10,672.50"  "73,083.00"  "2,332.00"   "1,904.00"  
##  [51] "57,860.00"  "23,420.00"  "70"         "43,475.84"  "86,726.78" 
##  [56] "4,488.00"   "17,036.00"  "2,216.00"   "26,467.20"  "33,552.58" 
##  [61] "23"         "19,052.00"  "45,428.00"  "37,985.90"  "88,667.00" 
##  [66] "67,001.30"  "111,602.71" "0"          "850"        "9,427.00"  
##  [71] "72,240.58"  "2,536.00"   "2,198.00"   "55,162.00"  "22,489.50" 
##  [76] "157.5"      "44,264.16"  "87,139.53"  "4,207.00"   "17,000.00" 
##  [81] "2,326.00"   "23,504.05"  "30,731.96"  "24"         "18,159.00" 
##  [86] "47,308.00"  "39,000.64"  "84,658.70"  "69,332.10"  "106,419.57"
##  [91] "10"         "850"        "8,441.74"   "66,331.61"  "2,810.00"  
##  [96] "2,081.50"   "54,246.42"  "22,350.00"  "157.5"      "37,478.87" 
## [101] "78,792.21"  "4,100.00"   "16,577.00"  "2,578.00"   "24,263.80" 
## [106] "21,520.45"  "40"         "20,139.30"  "44,733.64"  "37,282.04" 
## [111] "93,145.35"  "68,038.40"  "112,221.14" "870"        "6,698.20"  
## [116] "54,871.88"  "2,882.50"   "2,322.00"   "56,825.00"  "22,911.00" 
## [121] "70"         "37,175.06"  "0"          "79,809.34"  "5,143.00"  
## [126] "17,686.00"  "2,783.00"   "27,806.40"  "19,339.31"  "42"        
## [131] "21,109.83"  "45,588.03"  "33,947.15"  "90,904.48"  "69,456.71" 
## [136] "109,755.50" "659.04"     "9,289.05"   "60,264.29"  "2,905.84"  
## [141] "2,232.94"   "74,105.64"  "25,106.39"  "125.01"     "36,189.18" 
## [146] "118,200.88" "5,750.70"   "17,016.72"  "2,483.43"   "32,136.51" 
## [151] "25,332.45"  "24.27"      "21,203.03"  "39,615.60"  "38,613.68" 
## [156] "97,308.81"  "53,481.02"  "110,115.86" "936.34"     "9,834.39"  
## [161] "59,757.18"  "3,074.92"   "2,599.43"   "77,068.46"  "26,138.58" 
## [166] "136.88"     "33,623.54"  "128,273.15" "6,078.64"   "18,533.11" 
## [171] "2,739.71"   "33,608.32"  "23,724.20"  "101.16"     "21,462.81" 
## [176] "40,154.46"  "40,733.20"  "100,832.91" "56,035.94"  "109,649.61"
## [181] "1,065.07"   "10,461.85"  "58,376.40"  "3,410.56"   "2,752.31"  
## [186] "77,405.83"  "25,948.50"  "137.47"     "34,101.49"  "130,452.40"
## [191] "5,631.53"   "17,996.31"  "2,922.21"   "33,490.93"  "22,940.64" 
## [196] "128.65"     "21,491.21"  "41,732.03"  "42,679.11"  "103,368.73"
## [201] "54,938.79"  "105,666.60" "1,065.97"   "10,181.80"  "56,022.04" 
## [206] "3,392.22"   "2,671.04"   "78,421.95"  "25,530.59"  "134.96"    
## [211] "33,214.17"  "126,052.15" "5,531.20"   "17,745.80"  "2,924.89"  
## [216] "32,750.16"  "21,520.64"  "20,041.70"  "40,472.26"  "41,387.79" 
## [221] "100,328.77" "52,648.25"  "99,311.53"  "1,137.42"   "9,598.33"  
## [226] "51,854.59"  "3,408.69"   "2,436.63"   "80,289.56"  "25,158.80" 
## [231] "125.67"     "30,894.16"  "122,575.76" "5,340.80"   "18,129.50" 
## [236] "2,926.85"   "33,639.55"  "21,409.77"  "209.29"     "17,699.67" 
## [241] "37,334.16"  "42,327.26"  "96,018.89"  "51,470.86"  "98,038.15" 
## [246] "1,182.13"   "9,653.45"   "50,762.22"  "3,485.24"   "2,360.55"  
## [251] "82,085.54"  "23,915.45"  "140.33"     "29,085.24"  "122,002.46"
## [256] "4,810.97"   "17,414.32"  "2,761.01"   "33,465.54"  "20,873.04" 
## [261] "209.93"     "16,374.73"  "35,874.73"  "42,269.07"  "97,304.04" 
## [266] "48,305.31"
cafe_df[,2]
##   [1] "ANTIOQUIA"          "BOLIVAR"            "BOYACA"            
##   [4] "CALDAS"             "CAQUETA"            "CASANARE"          
##   [7] "CAUCA"              "CESAR"              "CHOCO"             
##  [10] "CUNDINAMARCA"       "HUILA"              "LA GUAJIRA"        
##  [13] "MAGDALENA"          "META"               "NARIÑO"            
##  [16] "NORTE DE SANTANDER" "PUTUMAYO"           "QUINDIO"           
##  [19] "RISARALDA"          "SANTANDER"          "TOLIMA"            
##  [22] "VALLE DEL CAUCA"    "ANTIOQUIA"          "BOLIVAR"           
##  [25] "BOYACA"             "CALDAS"             "CAQUETA"           
##  [28] "CASANARE"           "CAUCA"              "CESAR"             
##  [31] "CHOCO"              "CUNDINAMARCA"       "HUILA"             
##  [34] "LA GUAJIRA"         "MAGDALENA"          "META"              
##  [37] "NARIÑO"             "NORTE DE SANTANDER" "PUTUMAYO"          
##  [40] "QUINDIO"            "RISARALDA"          "SANTANDER"         
##  [43] "TOLIMA"             "VALLE DEL CAUCA"    "ANTIOQUIA"         
##  [46] "BOLIVAR"            "BOYACA"             "CALDAS"            
##  [49] "CAQUETA"            "CASANARE"           "CAUCA"             
##  [52] "CESAR"              "CHOCO"              "CUNDINAMARCA"      
##  [55] "HUILA"              "LA GUAJIRA"         "MAGDALENA"         
##  [58] "META"               "NARIÑO"             "NORTE DE SANTANDER"
##  [61] "PUTUMAYO"           "QUINDIO"            "RISARALDA"         
##  [64] "SANTANDER"          "TOLIMA"             "VALLE DEL CAUCA"   
##  [67] "ANTIOQUIA"          "ARAUCA"             "BOLIVAR"           
##  [70] "BOYACA"             "CALDAS"             "CAQUETA"           
##  [73] "CASANARE"           "CAUCA"              "CESAR"             
##  [76] "CHOCO"              "CUNDINAMARCA"       "HUILA"             
##  [79] "LA GUAJIRA"         "MAGDALENA"          "META"              
##  [82] "NARIÑO"             "NORTE DE SANTANDER" "PUTUMAYO"          
##  [85] "QUINDIO"            "RISARALDA"          "SANTANDER"         
##  [88] "TOLIMA"             "VALLE DEL CAUCA"    "ANTIOQUIA"         
##  [91] "ARAUCA"             "BOLIVAR"            "BOYACA"            
##  [94] "CALDAS"             "CAQUETA"            "CASANARE"          
##  [97] "CAUCA"              "CESAR"              "CHOCO"             
## [100] "CUNDINAMARCA"       "HUILA"              "LA GUAJIRA"        
## [103] "MAGDALENA"          "META"               "NARIÑO"            
## [106] "NORTE DE SANTANDER" "PUTUMAYO"           "QUINDIO"           
## [109] "RISARALDA"          "SANTANDER"          "TOLIMA"            
## [112] "VALLE DEL CAUCA"    "ANTIOQUIA"          "BOLIVAR"           
## [115] "BOYACA"             "CALDAS"             "CAQUETA"           
## [118] "CASANARE"           "CAUCA"              "CESAR"             
## [121] "CHOCO"              "CUNDINAMARCA"       "GUAVIARE"          
## [124] "HUILA"              "LA GUAJIRA"         "MAGDALENA"         
## [127] "META"               "NARIÑO"             "NORTE DE SANTANDER"
## [130] "PUTUMAYO"           "QUINDIO"            "RISARALDA"         
## [133] "SANTANDER"          "TOLIMA"             "VALLE DEL CAUCA"   
## [136] "ANTIOQUIA"          "BOLIVAR"            "BOYACA"            
## [139] "CALDAS"             "CAQUETA"            "CASANARE"          
## [142] "CAUCA"              "CESAR"              "CHOCO"             
## [145] "CUNDINAMARCA"       "HUILA"              "LA GUAJIRA"        
## [148] "MAGDALENA"          "META"               "NARIÑO"            
## [151] "NORTE DE SANTANDER" "PUTUMAYO"           "QUINDIO"           
## [154] "RISARALDA"          "SANTANDER"          "TOLIMA"            
## [157] "VALLE DEL CAUCA"    "ANTIOQUIA"          "BOLIVAR"           
## [160] "BOYACA"             "CALDAS"             "CAQUETA"           
## [163] "CASANARE"           "CAUCA"              "CESAR"             
## [166] "CHOCO"              "CUNDINAMARCA"       "HUILA"             
## [169] "LA GUAJIRA"         "MAGDALENA"          "META"              
## [172] "NARIÑO"             "NORTE DE SANTANDER" "PUTUMAYO"          
## [175] "QUINDIO"            "RISARALDA"          "SANTANDER"         
## [178] "TOLIMA"             "VALLE DEL CAUCA"    "ANTIOQUIA"         
## [181] "BOLIVAR"            "BOYACA"             "CALDAS"            
## [184] "CAQUETA"            "CASANARE"           "CAUCA"             
## [187] "CESAR"              "CHOCO"              "CUNDINAMARCA"      
## [190] "HUILA"              "LA GUAJIRA"         "MAGDALENA"         
## [193] "META"               "NARIÑO"             "NORTE DE SANTANDER"
## [196] "PUTUMAYO"           "QUINDIO"            "RISARALDA"         
## [199] "SANTANDER"          "TOLIMA"             "VALLE DEL CAUCA"   
## [202] "ANTIOQUIA"          "BOLIVAR"            "BOYACA"            
## [205] "CALDAS"             "CAQUETA"            "CASANARE"          
## [208] "CAUCA"              "CESAR"              "CHOCO"             
## [211] "CUNDINAMARCA"       "HUILA"              "LA GUAJIRA"        
## [214] "MAGDALENA"          "META"               "NARIÑO"            
## [217] "NORTE DE SANTANDER" "QUINDIO"            "RISARALDA"         
## [220] "SANTANDER"          "TOLIMA"             "VALLE DEL CAUCA"   
## [223] "ANTIOQUIA"          "BOLIVAR"            "BOYACA"            
## [226] "CALDAS"             "CAQUETA"            "CASANARE"          
## [229] "CAUCA"              "CESAR"              "CHOCO"             
## [232] "CUNDINAMARCA"       "HUILA"              "LA GUAJIRA"        
## [235] "MAGDALENA"          "META"               "NARIÑO"            
## [238] "NORTE DE SANTANDER" "PUTUMAYO"           "QUINDIO"           
## [241] "RISARALDA"          "SANTANDER"          "TOLIMA"            
## [244] "VALLE DEL CAUCA"    "ANTIOQUIA"          "BOLIVAR"           
## [247] "BOYACA"             "CALDAS"             "CAQUETA"           
## [250] "CASANARE"           "CAUCA"              "CESAR"             
## [253] "CHOCO"              "CUNDINAMARCA"       "HUILA"             
## [256] "LA GUAJIRA"         "MAGDALENA"          "META"              
## [259] "NARIÑO"             "NORTE DE SANTANDER" "PUTUMAYO"          
## [262] "QUINDIO"            "RISARALDA"          "SANTANDER"         
## [265] "TOLIMA"             "VALLE DEL CAUCA"
cafe_df[2,]
##   Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 2 2007      BOLIVAR     CAFE       502              446                 0.89
##   Produccion.Nacional..ton. Area.Nacional..ha.
## 2                      0.05               0.07
cafe_df[100,]
##     Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 100 2011 CUNDINAMARCA     CAFE 37,478.87        32,780.35                 0.87
##     Produccion.Nacional..ton. Area.Nacional..ha.
## 100                      5.12               5.26

#CON LA FUNCION TABLE LISTAMOS LAS FILAS DE LA VARIABLE SELECCIONADA

table(cafe_df$Produccion..ton.)
## 
##          0   1,089.74   1,128.32   1,338.56   1,388.13   1,617.20   1,629.25 
##          2          1          1          1          1          1          1 
##   1,650.41   1,656.96   1,672.60   1,688.60   1,718.25   1,747.51   1,933.00 
##          1          1          1          1          1          1          1 
##   1,950.84  10,200.84  10,221.69  10,826.24 101,201.88 102,147.00 102,403.24 
##          1          1          1          1          1          1          1 
## 103,703.00 104,336.56 104,609.42 105,563.88 105,976.19     105.93  11,035.85 
##          1          1          1          1          1          1          1 
##  11,937.90 111,452.91 112,322.38 113,505.20 115,267.98 115,874.98 119,970.68 
##          1          1          1          1          1          1          1 
##         12  12,012.98  12,214.54  12,332.00  12,770.00 120,365.77 120,500.80 
##          1          1          1          1          1          1          1 
## 121,253.38     124.67     125.42 129,052.51  13,276.08  13,278.50  13,301.60 
##          1          1          1          1          1          1          1 
##  13,412.80  13,593.24  13,593.25  13,600.00  13,841.45 131,316.47 133,787.95 
##          1          1          1          1          1          1          1 
## 135,971.20 136,161.86  14,005.00  14,017.00  14,096.05  14,943.62        140 
##          1          1          1          1          1          1          1 
## 140,398.62 141,898.91 145,154.42 145,168.10  15,050.27  15,108.55  15,185.79 
##          1          1          1          1          1          1          1 
##      158.2     158.85  16,628.14  16,691.31  16,935.63      16.87     160.62 
##          1          1          1          1          1          1          1 
##  17,031.09  17,739.03  18,030.13  18,792.05     181.42  19,590.10  19,994.35 
##          1          1          1          1          1          1          1 
##   2,023.50   2,048.40   2,079.70   2,133.10   2,134.00   2,188.92   2,221.90 
##          1          1          1          1          1          1          1 
##   2,328.90   2,332.00   2,340.40   2,393.00   2,446.38   2,469.00   2,503.81 
##          1          1          1          1          1          1          1 
##   2,528.40   2,533.75   2,564.86   2,626.73   2,638.88   2,902.50   2,958.70 
##          1          1          1          1          1          1          1 
##   2,990.91  20,267.64  20,599.27  20,814.11      205.9  21,065.00  21,985.00 
##          1          1          1          1          1          1          1 
##  22,089.82  22,111.65  22,240.81  22,518.42  22,649.03  23,271.89  23,409.44 
##          1          1          1          1          1          1          1 
##  23,471.69  23,669.00  23,791.30  24,073.95  24,594.10  24,694.56  24,993.74 
##          1          1          1          1          1          1          1 
##  25,118.55  25,426.00  26,311.61       26.7  27,094.16  27,487.71  28,077.94 
##          1          1          1          2          1          1          1 
##  28,606.96     282.18      289.5  29,016.75  29,469.52      292.6   3,206.35 
##          1          1          1          1          1          1          1 
##   3,322.42   3,434.30   3,447.31   3,516.80   3,749.27   3,861.63   3,877.62 
##          1          1          1          1          1          1          1 
##   3,923.80  30,227.02  30,786.41  31,165.15  31,262.50  31,413.34  31,770.05 
##          1          1          1          1          1          1          1 
##  32,321.56  32,580.24  32,780.35  33,729.14  33,943.39         34  34,512.79 
##          1          1          1          1          1          1          1 
##  35,004.18  35,679.42       35.6  36,607.56  36,989.43  37,020.90  37,118.07 
##          1          1          1          1          1          1          1 
##  37,214.80  39,073.92     395.07   4,013.11   4,317.50   4,387.19   4,981.59 
##          1          1          1          1          1          1          1 
##  41,645.39  42,719.53  42,948.40        446  45,113.00  45,918.75       45.8 
##          1          1          1          1          1          1          1 
##  46,779.71  47,215.69  47,221.00  47,304.16  47,357.02  47,512.36  48,073.00 
##          1          1          1          1          1          1          1 
##       48.4  49,042.31  49,667.88  49,799.28   5,108.33   5,280.40   5,591.05 
##          1          1          1          1          1          1          1 
##   5,643.39  50,588.14  51,348.00  51,687.80        510  53,288.42  53,648.00 
##          1          1          1          1          2          1          1 
##  54,115.96  54,908.68  55,918.71  56,303.92  57,067.08  57,583.56  58,634.19 
##          1          1          1          1          1          1          1 
##   6,364.41  60,079.00     606.93  61,190.55  62,711.08  62,869.38  63,365.76 
##          1          1          1          1          1          1          1 
##  65,475.63  65,666.43      652.5  66,661.14  67,231.37         68  68,668.20 
##          1          1          1          1          1          1          1 
##  68,670.96  69,496.65  69,618.24   7,083.07   7,638.99   7,780.34        711 
##          1          1          1          1          1          1          1 
##  72,091.00  72,842.55     734.91     748.97      76.04  77,215.36  78,254.77 
##          1          1          1          1          1          1          1 
##  78,805.87      78.75   8,567.97  81,668.22  83,626.44  85,027.49  85,150.66 
##          1          1          1          1          1          1          1 
##  85,212.64  86,453.62  86,884.00  87,642.49  88,633.10   9,501.54   9,547.30 
##          1          1          1          1          1          1          1 
##   9,583.80   9,683.10  91,621.30  92,815.00  94,230.20  94,556.71  95,957.90 
##          1          1          1          1          1          1          1 
##  97,451.31  97,922.49         98 
##          1          1          2
table(cafe_df$Rendimiento..ha.ton.)
## 
##    0  0.3 0.38 0.45 0.47 0.49 0.51 0.52 0.55 0.57 0.59  0.6 0.62 0.63 0.64 0.65 
##    2    1    1    2    1    1    1    1    1    4    2   10    7    1    1    7 
## 0.66 0.67 0.69  0.7 0.71 0.72 0.74 0.75 0.76 0.77 0.78 0.79  0.8 0.81 0.82 0.83 
##    5    2    5    1    2    2    2    6    2    4    2    5    8    3    4    1 
## 0.84 0.85 0.86 0.87 0.88 0.89  0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 
##    1    7    4    2    2    6    2    5    2    3    3    2    5    5    3    5 
##    1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09  1.1 1.11 1.12 1.13 1.14 1.15 
##    5    2    2    2    2    4    5    3    5    5    5    4    5    3    4    9 
## 1.16 1.17 1.18 1.19  1.2 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29  1.3 1.32 
##    3    3    2    4    2    2    2    1    2    1    1    1    2    1    2    2 
## 1.33 1.35 1.37 1.38  1.4 1.41 1.44 1.45 1.47  1.5 1.52 1.53 1.79    2 
##    1    2    1    1    1    1    1    1    1    1    2    1    1    1

#PODEMOS VER LA UTILIDAD E INFORMACION DE LA FUNCION

help (subset)

#CON LA FUNCION SUBSET PODEMOS SELECCIONAR POR VARIABLES EN NUESTRO DATAFRAME

subset(cafe_df, subset = Departamento == 'HUILA')
##     Anio Departamento Producto  Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 11  2007        HUILA     CAFE  89,661.56       129,052.51                 1.44
## 33  2008        HUILA     CAFE  89,131.20       131,316.47                 1.47
## 55  2009        HUILA     CAFE  86,726.78       104,609.42                 1.21
## 78  2010        HUILA     CAFE  87,139.53       104,336.56                 1.20
## 101 2011        HUILA     CAFE  78,792.21        85,150.66                 1.08
## 124 2012        HUILA     CAFE  79,809.34        85,212.64                 1.07
## 146 2013        HUILA     CAFE 118,200.88       115,874.98                 0.98
## 168 2014        HUILA     CAFE 128,273.15       135,971.20                 1.06
## 190 2015        HUILA     CAFE 130,452.40       145,168.10                 1.11
## 212 2016        HUILA     CAFE 126,052.15       145,154.42                 1.15
## 233 2017        HUILA     CAFE 122,575.76       133,787.95                 1.09
## 255 2018        HUILA     CAFE 122,002.46       136,161.86                 1.12
##     Produccion.Nacional..ton. Area.Nacional..ha.
## 11                      15.57              11.70
## 33                      15.85              11.75
## 55                      14.76              11.49
## 78                      13.39              11.71
## 101                     13.30              11.06
## 124                     13.60              11.23
## 146                     17.77              15.31
## 168                     18.67              16.12
## 190                     17.07              16.28
## 212                     17.00              16.21
## 233                     15.71              16.27
## 255                     15.91              16.43
subset(cafe_df, subset = Departamento == 'META')
##     Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 14  2007         META     CAFE  2,048.00         1,617.20                 0.79
## 36  2008         META     CAFE  2,146.00         1,656.96                 0.77
## 58  2009         META     CAFE  2,216.00         1,672.60                 0.75
## 81  2010         META     CAFE  2,326.00         2,221.90                 0.96
## 104 2011         META     CAFE  2,578.00         2,533.75                 0.98
## 127 2012         META     CAFE  2,783.00         2,133.10                 0.77
## 149 2013         META     CAFE  2,483.43         1,650.41                 0.66
## 171 2014         META     CAFE  2,739.71         1,950.84                 0.71
## 193 2015         META     CAFE  2,922.21         3,206.35                 1.10
## 215 2016         META     CAFE  2,924.89         3,322.42                 1.14
## 236 2017         META     CAFE  2,926.85         4,013.11                 1.37
## 258 2018         META     CAFE  2,761.01         3,877.62                 1.40
##     Produccion.Nacional..ton. Area.Nacional..ha.
## 14                       0.20               0.27
## 36                       0.20               0.28
## 58                       0.24               0.29
## 81                       0.29               0.31
## 104                      0.40               0.36
## 127                      0.34               0.39
## 149                      0.25               0.32
## 171                      0.27               0.34
## 193                      0.38               0.36
## 215                      0.39               0.38
## 236                      0.47               0.39
## 258                      0.45               0.37
subset(cafe_df, subset = Anio == '2009')
##    Anio       Departamento Producto  Area..ha. Produccion..ton.
## 45 2009          ANTIOQUIA     CAFE 112,420.20       103,703.00
## 46 2009            BOLIVAR     CAFE        770            292.6
## 47 2009             BOYACA     CAFE  10,672.50         8,567.97
## 48 2009             CALDAS     CAFE  73,083.00        81,668.22
## 49 2009            CAQUETA     CAFE   2,332.00         2,332.00
## 50 2009           CASANARE     CAFE   1,904.00         2,079.70
## 51 2009              CAUCA     CAFE  57,860.00        47,221.00
## 52 2009              CESAR     CAFE  23,420.00        12,770.00
## 53 2009              CHOCO     CAFE         70            78.75
## 54 2009       CUNDINAMARCA     CAFE  43,475.84        37,118.07
## 55 2009              HUILA     CAFE  86,726.78       104,609.42
## 56 2009         LA GUAJIRA     CAFE   4,488.00         2,340.40
## 57 2009          MAGDALENA     CAFE  17,036.00        13,412.80
## 58 2009               META     CAFE   2,216.00         1,672.60
## 59 2009             NARIÑO     CAFE  26,467.20        27,487.71
## 60 2009 NORTE DE SANTANDER     CAFE  33,552.58        10,221.69
## 61 2009           PUTUMAYO     CAFE         23             26.7
## 62 2009            QUINDIO     CAFE  19,052.00        21,985.00
## 63 2009          RISARALDA     CAFE  45,428.00        53,648.00
## 64 2009          SANTANDER     CAFE  37,985.90        26,311.61
## 65 2009             TOLIMA     CAFE  88,667.00        88,633.10
## 66 2009    VALLE DEL CAUCA     CAFE  67,001.30        62,711.08
##    Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 45                 0.92                     14.63              14.90
## 46                 0.38                      0.04               0.10
## 47                 0.80                      1.21               1.41
## 48                 1.12                     11.52               9.68
## 49                 1.00                      0.33               0.31
## 50                 1.09                      0.29               0.25
## 51                 0.82                      6.66               7.67
## 52                 0.55                      1.80               3.10
## 53                 1.13                      0.01               0.01
## 54                 0.85                      5.24               5.76
## 55                 1.21                     14.76              11.49
## 56                 0.52                      0.33               0.59
## 57                 0.79                      1.89               2.26
## 58                 0.75                      0.24               0.29
## 59                 1.04                      3.88               3.51
## 60                 0.30                      1.44               4.45
## 61                 1.16                      0.00               0.00
## 62                 1.15                      3.10               2.52
## 63                 1.18                      7.57               6.02
## 64                 0.69                      3.71               5.03
## 65                 1.00                     12.50              11.75
## 66                 0.94                      8.85               8.88
subset(cafe_df, subset = Anio == '2012')
##     Anio       Departamento Producto  Area..ha. Produccion..ton.
## 113 2012          ANTIOQUIA     CAFE 112,221.14        91,621.30
## 114 2012            BOLIVAR     CAFE        870            652.5
## 115 2012             BOYACA     CAFE   6,698.20         4,981.59
## 116 2012             CALDAS     CAFE  54,871.88        54,115.96
## 117 2012            CAQUETA     CAFE   2,882.50         2,446.38
## 118 2012           CASANARE     CAFE   2,322.00         1,718.25
## 119 2012              CAUCA     CAFE  56,825.00        50,588.14
## 120 2012              CESAR     CAFE  22,911.00        19,994.35
## 121 2012              CHOCO     CAFE         70              140
## 122 2012       CUNDINAMARCA     CAFE  37,175.06        30,786.41
## 123 2012           GUAVIARE     CAFE          0                0
## 124 2012              HUILA     CAFE  79,809.34        85,212.64
## 125 2012         LA GUAJIRA     CAFE   5,143.00         3,434.30
## 126 2012          MAGDALENA     CAFE  17,686.00        14,096.05
## 127 2012               META     CAFE   2,783.00         2,133.10
## 128 2012             NARIÑO     CAFE  27,806.40        28,077.94
## 129 2012 NORTE DE SANTANDER     CAFE  19,339.31        12,214.54
## 130 2012           PUTUMAYO     CAFE         42             48.4
## 131 2012            QUINDIO     CAFE  21,109.83        18,030.13
## 132 2012          RISARALDA     CAFE  45,588.03        36,989.43
## 133 2012          SANTANDER     CAFE  33,947.15        23,271.89
## 134 2012             TOLIMA     CAFE  90,904.48        85,027.49
## 135 2012    VALLE DEL CAUCA     CAFE  69,456.71        61,190.55
##     Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 113                 0.82                     14.62              15.80
## 114                 0.75                      0.10               0.12
## 115                 0.74                      0.79               0.94
## 116                 0.99                      8.63               7.72
## 117                 0.85                      0.39               0.41
## 118                 0.74                      0.27               0.33
## 119                 0.89                      8.07               8.00
## 120                 0.87                      3.19               3.22
## 121                 2.00                      0.02               0.01
## 122                 0.83                      4.91               5.23
## 123                 0.00                      0.00               0.00
## 124                 1.07                     13.60              11.23
## 125                 0.67                      0.55               0.72
## 126                 0.80                      2.25               2.49
## 127                 0.77                      0.34               0.39
## 128                 1.01                      4.48               3.91
## 129                 0.63                      1.95               2.72
## 130                 1.15                      0.01               0.01
## 131                 0.85                      2.88               2.97
## 132                 0.81                      5.90               6.42
## 133                 0.69                      3.71               4.78
## 134                 0.94                     13.57              12.80
## 135                 0.88                      9.76               9.78
subset(cafe_df, subset=Anio == '2009', 
       select=c('Rendimiento..ha.ton.', 'Produccion.Nacional..ton.'))
##    Rendimiento..ha.ton. Produccion.Nacional..ton.
## 45                 0.92                     14.63
## 46                 0.38                      0.04
## 47                 0.80                      1.21
## 48                 1.12                     11.52
## 49                 1.00                      0.33
## 50                 1.09                      0.29
## 51                 0.82                      6.66
## 52                 0.55                      1.80
## 53                 1.13                      0.01
## 54                 0.85                      5.24
## 55                 1.21                     14.76
## 56                 0.52                      0.33
## 57                 0.79                      1.89
## 58                 0.75                      0.24
## 59                 1.04                      3.88
## 60                 0.30                      1.44
## 61                 1.16                      0.00
## 62                 1.15                      3.10
## 63                 1.18                      7.57
## 64                 0.69                      3.71
## 65                 1.00                     12.50
## 66                 0.94                      8.85
subset(cafe_df, subset=Departamento == 'CUNDINAMARCA', 
       select=c('Rendimiento..ha.ton.', 'Produccion.Nacional..ton.'))
##     Rendimiento..ha.ton. Produccion.Nacional..ton.
## 10                  0.78                      4.07
## 32                  1.79                      9.44
## 54                  0.85                      5.24
## 77                  0.84                      4.78
## 100                 0.87                      5.12
## 122                 0.83                      4.91
## 145                 0.69                      3.83
## 167                 0.75                      3.45
## 189                 0.91                      3.66
## 211                 0.95                      3.68
## 232                 1.10                      3.99
## 254                 1.12                      3.81
subset(cafe_df, subset=Departamento == 'CUNDINAMARCA' & Anio <= '2012', 
       select=c( 'Produccion.Nacional..ton.', 'Area.Nacional..ha.', 'Rendimiento..ha.ton.'))
##     Produccion.Nacional..ton. Area.Nacional..ha. Rendimiento..ha.ton.
## 10                       4.07               5.61                 0.78
## 32                       9.44               5.75                 1.79
## 54                       5.24               5.76                 0.85
## 77                       4.78               5.95                 0.84
## 100                      5.12               5.26                 0.87
## 122                      4.91               5.23                 0.83

#PONEMOS LA INSTRUCCION EN UNA VARIABLE Y LO GRAFICAMOS

Grupo_Antioquia<-subset(cafe_df, subset=Departamento == 'ANTIOQUIA' & Anio <= '2012', 
                        select=c( 'Produccion.Nacional..ton.', 'Area.Nacional..ha.', 'Rendimiento..ha.ton.'))
Grupo_Antioquia
##     Produccion.Nacional..ton. Area.Nacional..ha. Rendimiento..ha.ton.
## 1                       14.54              14.66                 1.07
## 23                      13.70              15.13                 0.99
## 45                      14.63              14.90                 0.92
## 67                      15.56              14.99                 1.09
## 90                      18.00              14.94                 1.08
## 113                     14.62              15.80                 0.82
plot(Grupo_Antioquia)

#PODEMOS VER SI EXISTEN DATOS NULOS EN EL DATAFRAME

is.na(cafe_df)
##         Anio Departamento Producto Area..ha. Produccion..ton.
##   [1,] FALSE        FALSE    FALSE     FALSE            FALSE
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##        Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
##   [1,]                FALSE                     FALSE              FALSE
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## [265,]                FALSE                     FALSE              FALSE
## [266,]                FALSE                     FALSE              FALSE
is.na(cafe_df$Area..ha.)
##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE
is.na(cafe_df$Rendimiento..ha.ton.)
##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE

#NOS MUESTRA LA SUMA DE LOS VALORES NULOS EN EL DATAFRAME

rowSums(is.na(cafe_df))
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0 0 0 0 0 0 0

#NOS MUESTRA LA SUMA TOTAL DE TODOS LOS VALORES NULOS

sum(rowSums(is.na(cafe_df)))
## [1] 0

#VISUALIZAMOS HISTOGRAMAS POR VARIABLE

plot(cafe_df$Rendimiento..ha.ton.)

#GRAFICAMOS LA VARIABLE Y SELECCIONAMOS ALGUNOS DETALLES

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="CANTIDAD",
     type ="p", # p indica puntos de dispersion
     col ="red",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="Indices",
     type ="b", # b indica lineas uniendo los puntos de dispersion
     col ="red",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="Indices",
     type ="o", # b indica lineas sobreindicadas sobre los puntos de dispersion
     col ="blue",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="Indices",
     type ="h", # b indica lineas tipo histograma sobre los puntos de dispersion
     col ="blue",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="Indices",
     type ="s", # s funcion escalera (horizontal a vertical)
     col ="blue",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion", 
     sub ="Indices",
     type ="l", # l indica lineas 
     col ="blue",
     xlab ="cantidad",
     ylab = "Rendimiento (ha/ton)")

#GRAFICAS POR VARIABLES

plot(x = cafe_df$Area..ha.,  y = cafe_df$Rendimiento..ha.ton.)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

plot(x = cafe_df$Area..ha.,  y = cafe_df$Area.Nacional..ha.)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

plot(x = cafe_df$Anio,  y = cafe_df$Rendimiento..ha.ton.)

plot(x = cafe_df$Anio,  y = cafe_df$Produccion..ton.)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

plot(x = cafe_df$Area..ha.,  y = cafe_df$Produccion.Nacional..ton.)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

plot(cafe_df$Area..ha.)
## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

#REPRESENTA LA FRECUENCIA DE LOS DATOS EN UN HISTOGRAMA

hist(cafe_df$Rendimiento..ha.ton., main ="Histograma", 
     sub ="Rendimiento (ha/ton)",
     col ="red",
     xlab ="Cantidad (miles) ",
     ylab = "Frecuencia")

#DIAGRAMA DE BARRAS DE LA COLUMNA RENDIMIENTO

barplot(table(cafe_df$Rendimiento..ha.ton.))

#PARA VER COMO SE DISTRUBUYEN LOS DATOS PODEMOS USAR LA FUNCION DENSITY, LA DENSIDAD ES UNA VERSION SUAVIZASA DEL HISTOGRAMA, NOS PERMITE OBSERVAR SI LOS DATOS SE COMPORTAN COMO UNA DISTRIBUCION NORMAL

plot(density(cafe_df$Rendimiento..ha.ton.), main="Densidad para el Rendimiento (ha/ton)")

plot(density(cafe_df$Produccion.Nacional..ton.), main="Densidad para la produccion Nacional (ton)")

plot(density(cafe_df$Area.Nacional..ha.), main="Densidad para el Area Nacional (ha)")

#HACEMOS UN GRAFICO DE PASTEL

pie(table(cafe_df$Departamento[1:10]))

pie(table(cafe_df$Rendimiento..ha.ton.[1:10]))

#LOS BOXPLOTS O DIAGRAMAS DE CAJA SE CONTRIBUYEN A PARTIR DE LOS PERCENTILES. #SE CONSTRUYE UN RECTANGULO USANDO ENTRE EL PRIMER Y EL TERCER CUARTIL. #LA ALTURA DEL RECTANGULO ES EL RANGO INTERCUARTIL. #LA MEDIANA ES UNA LINEA QUE DIVIDE EL RECTANGULO- #LOS VALORES MAS EXTREMOS QUE EL LARGO DE LOS BRAZOS SON CONSIDERADOS ATIPICOS #EL BOXPLOT NOS ENTREGA INFORMACION SOBRE LA SIMETRIA DE LA DISTRIBUCION DE LOS DATOS, SI LA MEDIANA NO ESTA EN EL CENTRO DEL RECTANGULO LA DISTRIBUCION NO ES SIMETRICRA. #SON UTILES PARA VER LA PRESENCIA DE VALORES ATIPICOS O OUTLERS

boxplot(cafe_df$Rendimiento..ha.ton.,ylab="Rendimiento (ha/ton)")

boxplot(cafe_df$Produccion.Nacional..ton.,ylab="Produccion Nacional (ton)")

#SELECCIONAR POR RANGOS

boxplot(x=cafe_df$Rendimiento..ha.ton.[1:10],main="Rendimiento (ha/ton)")

#CON LA FUNCION HEXBIN SE PUEDE MEJORAR LA PRESENTACION Y LECTURA DE LOS GRAFICOS. #SE CREA EL OBJETO BIN CON LA RELACION DE DOS VARIABLES

bin1<-hexbin(cafe_df$Produccion..ton.,cafe_df$Rendimiento..ha.ton., xbins=10)
## Warning in xy.coords(x, y, xl, yl): NAs introduced by coercion
plot(bin1)

bin2<-hexbin(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., xbins=10)

plot(bin2)

#HALLAMOS LA CORRELACION ENTRE LAS VARIABLES POR CORRELACION DE PEARSON- #INDICAMOS LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN

cor(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., use="complete.obs")
## [1] 0.2806767

#PARA CALCULAR LA MODA SELECCIONANDO LAS VARIABLES EN EL DATAFRAME “cafe_df”

moda=function(var){
        frec.var<-table(var)
        valor=which(frec.var==max(frec.var)) #Elemento con el valor
        names(valor)}

which.max(cafe_df$Rendimiento..ha.ton.)
## [1] 121

#SEGUIMOS ESTUDIANDO EL COMPORTAMIENTO O DISTRIBUCION DE LOS DATOS O LA INFORMACION CON GRAFICOS DE DISPERSION. #GRAFICO DE DISPERSION DEL COMPORTAMIENTO DE LA PRODUCCION VS RENDIMIENTO, PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS

qplot(Produccion..ton., Rendimiento..ha.ton., data = cafe_df,
      main ="Grafico de Dispersion",
      col = "Red",
      xlab ="Produccion (miles ton)",
      ylab = "Rendimiento (ha/ton)")

#GRAFICO DE DISPERSION SEL COMPORTAMIENTO DE LA PRODUCCION NACIONAL VS RENDIMIENTO.

qplot(Produccion.Nacional..ton., Rendimiento..ha.ton., data = cafe_df,
      main ="Grafico de Dispersion",
      col = "Red",
      xlab ="Produccion Nacional (miles ton)",
      ylab = "Rendimiento (ha/ton)")

#COEFICIENTE DE CORRELACION DE PEARSON ENTRE LA PRODUCCION NACIONAL Y EL RENDIMIENTO. #HALLAS LA CORRELACION ENTRE LAS VARIABLES POR CORRELACION DE PEARSON #INDICAMOS QUE UTILIZA LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN

cor(cafe_df$Produccion.Nacional..ton.,cafe_df$Rendimiento..ha.ton., use="complete.obs")
## [1] 0.3855697

#GRAFICO DE DISPERSION DEL COMPORTAMIENTO ENTRE EL AREA NACIONAL VS EL RENDIMIENTO. PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS

qplot(Area.Nacional..ha., Rendimiento..ha.ton., data = cafe_df,
      main ="Grafico de Dispersion",
      col = "Red",
      xlab ="Area Nacional ha",
      ylab = "Rendimiento (ha/ton)")

#COEFICIENTE DE CORRELACION DE PEARSON #COEFICIENTE DE CORRELACIÓN ENTRE EL AREA NA-CIONAL Y EL RENDIMIENTO #HALLAR LA CORRELACION ENTRE LAS VARIABLES POR METODO CORRELACION DE PEARSON #INDICAMOS QUE UTILIZA LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN

cor(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., use="complete.obs")
## [1] 0.2806767
qplot(Anio, Rendimiento..ha.ton., data = cafe_df,
       main ="Grafico de Dispersion",
       col = "Red",
       xlab ="Area Nacional ha",
       ylab = "Rendimiento (ha/ton)")

#ESPECIFICAR EL DATAFRAME ES EL PRIMER ARGUMENTO EN LA FUNCIÓN GGPLOT # DENTRO DE AES() ESCRIBIMOS LAS VARIABLES (X,Y) QUE QUEREMOS GRAFICAR # PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS # SE PUEDE UTILIZAR EL PARAMETRO COLOR PARA REPRESENTAR UNA TERCERA VARIABLE # GRÁFICO DE DISPERSION: AREA NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_point(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE LINEAS: AREA NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_line(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE DISPERSION: PRODUCCION NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_point(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE LINEAS: PRODUCCION NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_line(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#OTRA FORMA DE GRAFICO DE LINEAS SIN REPRESENTAR LOS AÑOS

data(cafe_df)
## Warning in data(cafe_df): data set 'cafe_df' not found
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) + 
        geom_line()

#HISTOGRAMA: AREA NACIONAL

ggplot(cafe_df) + 
        geom_histogram(aes(x = Area.Nacional..ha., color="red"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#HISTOGRAMA PRODUCCION NACIONAL

ggplot(cafe_df) + 
        geom_histogram(aes(x = Produccion.Nacional..ton., color="red"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#HISTOGRAMA RENDIMIENTO

ggplot(cafe_df) + 
        geom_histogram(aes(x = Rendimiento..ha.ton., color="red"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#GRAFICO DE LINEAS: PRODUCCION NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_line(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE CAJAS: PRODUCCION NACIONA VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_boxplot(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

#GRAFICO DE CAJAS: AREA NACIONAS VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_boxplot(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

#GRAFICO DE DISPERSION JITTER: AREA NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_jitter(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE DISPERSION PRODUCCION NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_point(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#GRAFICO DE DISPERCION JITTER: PRODUCCION NACIONAL VS RENDIMIENTO

ggplot(cafe_df) + 
        geom_jitter(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#OTRA FORMA CON GRAFICO DE DISPERSION SIN REPRESENTAR LOS AÑOS

data(cafe_df)
## Warning in data(cafe_df): data set 'cafe_df' not found
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) + 
        geom_point()

#OTRA FORMA CON GRAFICO DE LINEAS SIN REPRESENTAR LOS AÑOS

data(cafe_df)
## Warning in data(cafe_df): data set 'cafe_df' not found
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) + 
        geom_line()

#COMPARANDO CUANDO TIENE REPRESENTADO LOS AÑOS A CONTINUACION

ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio)) + 
        geom_line()

#TAMBIEN SE PUEDE REALIZAR DISPERSION EN PANELES POR AÑOS. #PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS

ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton.)) + 
  geom_jitter() +
  facet_grid(.~ Anio)

#PANELES SEPARADOS #TAMBIEN SE PUEDE REALIZAR DISPERSION EN PANELES SEPARADOS POR AÑOS

ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton.)) + 
  geom_point() +
  facet_wrap(.~ Anio)

ggplot(cafe_df, aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton.)) + 
  geom_point() +
  facet_wrap(.~ Anio)

##COMPARANDO EL ANTERIOR CON EL GRAFICO DE DISPERSION SIN AÑOS # OTRA FORMA CON GRAFICO DE DISPERSION SIN REPRESENTAR LOS AÑOS

data(cafe_df)
## Warning in data(cafe_df): data set 'cafe_df' not found
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) + 
  geom_point()

#COMPARAR CON GRAFICO DE DISPERSION PRODUCCION NACIONAL VS RENDIMIENTO CON AÑOS

ggplot(cafe_df) + 
  geom_jitter(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))

#AHORA SE UTILIZARA UN SUAVIZADOR #LA GRAFICA LINEAL SUAVIZADA

ggplot(cafe_df, aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton.)) + 
  geom_point() +
  facet_wrap(.~ Anio) +
  geom_smooth(span = 3)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

FIN